When buying a house, you don't only walk through 10% of it. When car shopping, do you only look at 10% of the car? Why then, would you only look at 10% of transactions during the due diligence phase of buying a multi-million dollar business?
In the high-stakes world of Mergers & Acquisitions, the margin for error is razor-thin. Transaction advisory teams regularly spend upwards of $300,000 on standard due diligence for a single acquisition, yet the reality is that traditional Quality of Earnings (QofE) reports still heavily rely on manual sampling — a process typically testing just 5% to 10% of a target company's financial data.
This outdated industry standard leaves a massive "Audit Gap" where sophisticated fraud, operational leakage, and manipulated EBITDA can thrive unseen. To truly protect capital, ensure the economic truth of a deal, and avoid the next multi-million-dollar disaster, deal teams should be using applied forensic analytics to scan 100% of a target's ledger and underlying data.
"The multi-million dollar failures of recent years prove that what you don't sample can and will hurt you."
The $175M Oversight: JPMorgan Chase & Frank
In 2021, JPMorgan Chase acquired the fintech startup Frank for $175 million, largely based on the company's claim of having 4.25 million users. Post-close, JPMC discovered a staggering reality: over 90% of the user base was completely fake. The founder had allegedly hired a data scientist to generate millions of synthetic names, emails, and birthdays to pass summary-level diligence.
- Instead of relying on unverified summary reports, a 100% data population scan would ingest and analyze the entire user database.
- Synthetic entity verification would cross-reference data with external enrichment sources to verify digital and physical footprints.
- Automated address clustering algorithms would instantly flag users tied to non-residential locations or the same P.O. Box, exposing the synthetic dataset in minutes.
- Analysis of server audit logs would identify the discrepancy between legitimate user activity and the fabricated records.
The $1.2M Leak: Optum's Ghost Employee Scheme
While synthetic acquisitions grab the biggest headlines, internal operational leakage quietly erodes EBITDA. A senior executive at Optum (UnitedHealth) managed to steal $1.2 million over three years before finally being detected. The executive orchestrated a scheme involving a "ghost employee" who performed no work but kicked back a portion of their salary, while simultaneously overbilling the company through a related-party consulting firm owned by the executive's brother.
- Traditional HR audits only check if a personnel file exists. Behavioral log correlation cross-references payroll records against physical and digital footprints.
- The system automatically flags an employee receiving a full salary with zero operational activity.
- Automated conflict of interest matching scans 100% of the ledger for shared identifiers — matching bank account numbers, beneficial owner names, or addresses between employees and vendors — instantly exposing the brother's related-party firm.
Enhancing Deal Certainty
The implementation of automated, full-population forensic analytics fundamentally shifts M&A due diligence from "trust-based" verification to "forensic-based" data truth. When deal teams transition from 5% manual sampling to analyzing every single transaction across the General Ledger, Accounts Payable, and Payroll, they aren't just checking a compliance box — they are protecting deal value and creating actionable pre-close leverage.
Identifying these anomalies before signing gives operating partners and buyers the empirical data needed to adjust purchase prices, negotiate escrows, or build precise Day-One remediation plans.
"The landscape of due diligence needs to change, and relying on sampling is no longer enough."
If you are interested in learning how applied forensic data analytics can bulletproof your next transaction, reach out to me directly to connect and learn more.
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Start a ConversationOriginally published on LinkedIn on May 6, 2026. Follow David Voorhees for more insights on forensic accounting and risk intelligence.